The present invention relates in general to a method of estimating crank angles and rotation speeds of an engine, and more particularly, to a method of processing a measured crank signal by a time based methodology and obtaining a feedback gain matrix by using pole placement method, solving Riccati formula, solving infinite time domain of Riccatic, so as to perform an estimation of crank angles and rotation speeds in a close-loop state. The present invention further relates to a method of discriminating a crank stroke for assisting the calculation of crank dynamic sampling signal.
The typical internal combustion machine (engine) is equipped with an engine control unit (ECU) to read the signals of crank angle, rotation speed and acceleration. The signal of the crank angle provides information of ignition timing control and fueling timing control, while the signals of crank rotation speed and acceleration can be used to estimate the engine indicative torque and engine failure diagnostics. The engine control unit detects the crank angle sensor mounted on a flywheel that synchronously rotating with the crank to detect the signal of crank rotation. However, as the signals detected by the sensor are easily interfered by external noises, the calculation result is unreliable.
The commonly used processing and calculation techniques for the crank signals include position-based method and time-based method. The position-based method requires a larger tooth number of the flywheel (such as 180 or 360 teeth) to obtain a precise crank angle position. Therefore, a massive processing capability of the engine control unit is demanded to avoid interrupt or insufficient memory space occurring during calculation. This method is thus more costly and more difficult to implement.
The time-based method requires relative smaller tooth number of the flywheel (such as 1, 4, 24 or 32 teeth) for calculating the crank angle of the engine. Therefore, this method provides a more economic approach for dynamically estimation of crank rotation. However, this method is susceptible to noises introduced to the crank signals such that error estimation often occurs.
To prevent the noise from interfering the crank signals, currently, a low pass filter (LPF) has been used to filter the noise of the crank angle sensor. When the calculated crank signal traveling through the low pass filter is greater than a default value, the low pass filter will output a voltage signal. The low pass filter creates a delay effect and when the noise is exceedingly larger, the noise is often output as the actual signal.
In the published Taiwanese Patent No. 1243904, The Applicant of the current application has disclosed a method which uses a Kalman filter to estimate crank angle and rotation speed. The method integrates an estimation formula and a Kalman filter of a measuring matrix into a rotation dynamic estimation system of an engine. In addition, the crank angle θ measured by an electric signal processing system, an engine torque estimation value {circumflex over (T)}br obtained from an engine torque estimation system, and a load torque estimation value {circumflex over (T)}load obtained from a load torque estimation system are used to estimate the crank angle and rotation speed, such that when the crank position sensor is interfered by noise, the influence on engine control can be reduced. This method requires the parameter of rotation inertia of the engine and viscosity coefficient. Any errors of these parameters will affect the estimation precision of the filter and the system robustness.
With regard to the calculation method of sublimes allocation and solving Riccati to obtain the feedback gain matrix and to discriminate the crank rotation stroke, although such arithmetic techniques have been disclosed, they have never been associated or applied in the close-loop estimation of engine crank angle and rotation speed or assisting process of crank dynamic sampling signals.